Masgent: An AI-assisted Materials Simulation Agent
Guanghen Liu, Songge Yang, Yu Zhong

TL;DR
Masgent is an AI-powered platform that simplifies materials simulations by enabling natural-language commands, automating workflows, and integrating advanced computational tools to accelerate materials discovery and research.
Contribution
It introduces Masgent, a unified platform leveraging large language models to automate and streamline complex materials simulation workflows.
Findings
Reduces simulation setup time from hours to seconds.
Integrates DFT, MLP, and ML tools within a single platform.
Enables natural-language interaction for complex simulation tasks.
Abstract
Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By…
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Taxonomy
TopicsMachine Learning in Materials Science · Catalysis and Oxidation Reactions · Inorganic Chemistry and Materials
